An emotion recognition method based on EWT-3D–CNN–BiLSTM-GRU-AT model

• Published in: Computers in biology and medicine Vol. 169; p. 107954
• Main Authors: Çelebi, Muharrem, Öztürk, Sıtkı, Kaplan, Kaplan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2024; Elsevier Limited
• Subjects: 3-D; Arousal; Artificial Intelligence; Artificial neural networks; Attention; Bidirectional long-short-term memory; Brain; Classification; Convolutional neural networks; Datasets; Deep learning; EEG; Electroencephalogram; Electroencephalography; Emotion recognition; Emotions; Empirical wavelet transform; Entropy; Fourier transforms; Fractals; Frequency dependence; Gated recurrent unit; Humans; Internal Medicine; Kurtosis; Machine learning; Methods; Neural networks; Neural Networks, Computer; Other; Physiology; Self-attention; Short term memory; Signal processing; Skewness; Success; Three dimensional models; Wavelet Analysis; Wavelet transforms; Deep learning; Emotion recognition; Gated recurrent unit; Empirical wavelet transform; Electroencephalogram; Self-attention; 3-D; Bidirectional long-short-term memory; Convolutional neural networks

This has become a significant study area in recent years because of its use in brain-machine interaction (BMI). The robustness problem of emotion classification is one of the most basic approaches for improving the quality of emotion recognition systems. One of the two main branches of these approac...